Soil Profile Images
Buy and sell soil profile images data. Cross-section photos of soil horizons with composition labels. Geotechnical AI classifies soil types from profile images for construction planning.
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Find Me This Data →Overview
What Is Soil Profile Images?
Soil profile images are cross-section photographs of exposed soil walls, captured after excavating pits approximately 1 meter deep. These frontal images reveal natural stratification into soil horizons through variations in color, texture, and structure. The visual data is georeferenced and paired with expert annotations documenting horizon-level morphological properties such as color, carbonate content, and depth markers. Profile-imaging methods provide cost-effective, objective collection of soil morphological features for field research and digital soil description. Deep learning and machine learning algorithms now automatically classify soil types and delineate horizons from these images, supporting geotechnical analysis for construction planning and agricultural assessment.
Market Data
3,349 images
Soil Profile Images in Leading Dataset
Source: arXiv
13,621 horizons across profiles
Annotated Horizons per Dataset
Source: arXiv
4,864 unique images
VITSoil Dataset Soil Images
Source: PubMed Central
4 orders (Alfisols, Entisols, Inceptisols, Mollisols)
Soil Orders in China Study
Source: ScienceDirect
2–8 horizons
Horizons per Soil Profile
Source: arXiv
Who Uses This Data
What AI models do with it.do with it.
Soil Scientists & Soil Survey
Rapid digital soil descriptions and quantitative delineation of soil horizons in-situ to support soil classification and pedogenesis research.
Agricultural Land Assessment
Evaluation of soil erosion, soil fertility, water content, and classification of soils for agricultural productivity across regional maps.
Geotechnical & Construction Planning
AI-driven classification of soil types from profile images to inform foundation design, excavation feasibility, and site geotechnical characterization.
Environmental & Remote Sensing
Integration with satellite data (Landsat-8, Sentinel-1A, MODIS) and georeferenced environmental variables for spatial soil mapping and land-use analysis.
What Can You Earn?
What it's worth.worth.
Small Dataset (50–200 images)
Varies
Pricing depends on annotation depth (color labels, horizon boundaries, morphological properties) and image resolution quality.
Medium Dataset (500–2,000 images)
Varies
Multi-horizon annotated datasets with geolocation and tabular metadata command premium rates.
Large Comprehensive Dataset (3,000+ images)
Varies
Production-grade datasets with 13,000+ annotated horizons, expert validation, and multi-modal tabular data attract institutional and AI training buyers.
What Buyers Expect
What makes it valuable.valuable.
Consistent Image Quality & Resolution
High resolution captures (e.g., 2048 × 1536 pixels or smartphone equivalents) with uniform frontal top-down framing, controlled illumination, and minimal shadows to ensure morphological feature visibility.
Accurate Horizon Annotation & Labeling
Expert-validated delineation of soil horizons with documented morphological properties including Munsell color, texture, structure, carbonate content, and depth markers.
Geotemporal Metadata & Context
Georeferenced location data, sampling depth, soil order classification, and field context (e.g., geological region, land use, climatic conditions) paired with each profile.
Dataset Diversity & Size
Multiple soil orders, geographic regions, and horizon types to ensure algorithm generalization; datasets of 1,000+ images with balanced class representation are preferred for deep learning.
Companies Active Here
Who's buying.buying.
Training soil classification models for crop yield prediction, precision farming, and land-use mapping.
Building AI tools for automated soil type identification and site feasibility assessment.
Developing deep learning frameworks for soil horizon delineation, pedogenesis research, and image-processing algorithms.
Integrating profile images with satellite and tabular data to create comprehensive soil databases and digital surveys.
FAQ
Common questions.questions.
What resolution and camera equipment are typical for soil profile images?
Images range from smartphone cameras (13-megapixel, 4160 × 2340 resolutions) to digital SLRs and stereo cameras (2048 × 1536 pixels or higher). Smartphones like Xiaomi Redmi 3s Prime and iPhones are widely used due to portability and cost-effectiveness. Controlled illumination and fixed distance (0.5–1 meter) improve consistency.
How deep are soil pits excavated for profile image capture?
Standard practice is to excavate pits approximately 1 meter deep to expose a vertical cross-section of the soil wall, revealing multiple horizons with natural stratification visible through color, texture, and structural variation.
What annotation data must accompany soil profile images?
Expert annotations include horizon-level morphological properties (color via Munsell charts, texture, structure, carbonate content), depth markers, geotemporal metadata (latitude, longitude, sampling date), soil order classification, and field context. Leading datasets include 13,000+ annotated horizons paired with tabular data.
How are deep learning models trained on soil profile images?
Models use image-recognition algorithms and clustering methods to identify horizons, quantify soil variation, and classify soil types. Data augmentation (e.g., expanding 160 original images to 2,400) improves training performance. Convolutional neural networks and transfer learning on platforms like ImageNet support automated horizon delineation and morphological feature detection.
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